(1) Technical Field
The present invention relates to a method and system for generic object detection using block-based feature computation and, more specifically, to a method and system for massively parallel computation of object feature sets according to an optimized clock-cycle matrix.
(2) Background
Computer vision-based object detection technology is becoming more widely used in visual surveillance, active safety, and threat detection areas. The recent advances in technologies related to low-cost cameras, mote networks, low cost computational resources, and advanced vision algorithms, have brought to fruition some of the systems that were hitherto infeasible. However, there is still a strong market-driven need for real-time, embedded, mobile, and low cost systems for many time-critical applications. State of the art object detection algorithms that work on both visible and infrared imagery have been successfully developed and are currently in use, but require significant processing time and resources.
State of the art technology for detecting objects of interest in both visible and infrared imagery is not completely real-time due to its complexity. Commercial chip vendors do not have efficient systems that can accomplish this task. Although there are a few companies with motion detection systems for camcorders and surveillance video, they all suffer from high still-frame processing time which causes gaps in video quality. One wavelet-based fast image detection algorithm, disclosed in Y. Owechko, S. Medasani, and N. Srinivasa, “Classifier Swarms for Human Detection in Infrared Imagery”, Proc. of the CVPR workshop on Object Tracking and Classification Beyond the Visible Spectrum (OTCBVS '04) 2004, has been shown to have a better performance in comparison with other detection algorithms.
Therefore, a continuing need exists for a fast object detection algorithm customized for a Very Large Scale Integration (VLSI) chip to improve processing speed and provide a real-time, embedded, mobile, and low-cost system for time-critical applications.